scholarly journals A new hat for librarians: providing REDCap support to establish the library as a central data hub

2018 ◽  
Vol 106 (1) ◽  
Author(s):  
Kevin Read ◽  
Fred Willie Zametkin LaPolla
Keyword(s):  
Data Management ◽  
Clinical Data ◽  
Medical Center ◽  
Academic Medical Center ◽  
Data Series ◽  
Management Support ◽  
Support Unit ◽  
Data Services ◽  
Service Gap ◽  
Central Data

Background: REDCap, an electronic data capture tool, supports good research data management, but many researchers lack familiarity with the tool. While a REDCap administrator provided technical support and a clinical data management support unit provided study design support, a service gap existed.Case Presentation: Librarians with REDCap expertise sought to increase and improve usage through outreach, workshops, and consultations. In collaboration with a REDCap administrator and the director of the clinical data management support unit, the role of the library was established in providing REDCap training and consultations. REDCap trainings were offered to the medical center during the library’s quarterly data series, which served as a springboard for offering tailored REDCap support to researchers and research groups.Conclusions: Providing REDCap support has proved to be an effective way to associate the library with data-related activities in an academic medical center and identify new opportunities for offering data services in the library. By offering REDCap services, the library established strong partnerships with the Information Technology Department, Clinical Data Support Department, and Compliance Office by filling in training gaps, while simultaneously referring users back to these departments when additional expertise was required. These new partnerships continue to grow and serve to position the library as a central data hub in the institution.

Accessibility and Openness to Diabetes Management Support via Mobile Phones: A Survey of People with Type 1 Diabetes Using Advanced Diabetes Technologies (Preprint)

2022 ◽  
Author(s):  
Yu Kuei Lin ◽  
Caroline Richardson ◽  
Iulia Dobrin ◽  
Rodica Pop-Busui ◽  
Gretchen Piatt ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Diabetes Management ◽  
Interactive Voice Response ◽  
Medical Center ◽  
Academic Medical Center ◽  
Text Messages ◽  
Management Support ◽  
Cross Sectional Survey ◽  
Cross Sectional

BACKGROUND Little is known about the feasibility of mobile health (mHealth) support among people with type 1 diabetes (T1D) using advanced diabetes technologies including continuous glucose monitors (CGMs) and hybrid closed-loop insulin pumps (HCLs). OBJECTIVE To evaluate patient access and openness to receiving mHealth diabetes support in people with T1D using CGMs/HCLs. METHODS We conducted a cross-sectional survey among T1D patients using CGMs or HCLs managed in an academic medical center. Participants reported information regarding their mobile device usage, cellular call/text message/internet connectivity, and openness to various channels of mHealth communication (smartphone applications or “apps”, text messages, and interactive voice response calls or IVR calls). Participants’ demographic characteristics and CGM data were collected from medical records. Analyses focused on differences in openness to mHealth and mHealth communication channels across groups defined by demographic variables and measures of glycemic control. RESULTS Among all participants (n=310; 64% female; mean age: 45 (SD:16)), 98% reported active cellphone use, and 80% were receptive to receiving mHealth support to improve glucose control. Among participants receptive to mHealth support, 98% were willing to share CGM glucose data for mHealth diabetes self-care assistance. Most (71%) were open to receiving messages via apps, 56% were open to text messages, and 12% were open to IVR calls. Older participants were more likely to prefer text messages (P=0.009) and IVR (P=0.03) than younger participants. CONCLUSIONS Most people with T1D who use advanced diabetes technologies have access to cell phones and are receptive to receiving mHealth support to improve diabetes control. CLINICALTRIAL Not applicable

scholarly journals Promoting Data Reuse and Collaboration at an Academic Medical Center

2015 ◽  
Vol 10 (1) ◽  
pp. 260-267 ◽  
Author(s):  
Kevin Read ◽  
Jessica Athens ◽  
Ian Lamb ◽  
Joey Nicholson ◽  
Sushan Chin ◽  
...  
Keyword(s):  
Data Management ◽  
Population Health ◽  
Data Sharing ◽  
Present State ◽  
Medical Center ◽  
Academic Medical Center ◽  
Data Reuse ◽  
Data Description ◽  
University Library ◽  
Academic Medical

A need was identified by the Department of Population Health (DPH) for an academic medical center to facilitate research using large, externally funded datasets. Barriers identified included difficulty in accessing and working with the datasets, and a lack of knowledge about institutional licenses. A need to facilitate sharing and reuse of datasets generated by researchers at the institution (internal datasets) was also recognized. The library partnered with a researcher in the DPH to create a catalog of external datasets, which provided detailed metadata and access instructions. The catalog listed researchers at the medical center and the main campus with expertise in using these external datasets in order to facilitate research and cross-campus collaboration. Data description standards were reviewed to create a set of metadata to facilitate access to both externally generated datasets, as well as the internally generated datasets that would constitute the next phase of development of the catalog. Interviews with a range of investigators at the institution identified DPH researchers as most interested in data sharing, therefore targeted outreach to this group was undertaken. Initial outreach resulted in additional external datasets being described, new local experts volunteering, proposals for additional functionality, and interest from researchers in inclusion of their internal datasets in the catalog. Despite limited outreach, the catalog has had ~250 unique page views in the three months since it went live. The establishment of the catalog also led to partnerships with the medical center’s data management core and the main university library. The Data Catalog in its present state serves a direct user need from the Department of Population Health to describe large, externally funded datasets. The library will use this initial strong community of users to expand the catalog and include internally generated research datasets. Future expansion plans will include working with DataCore and the main university library.

scholarly journals Accuracy of Infection Control Surveillance in Identifying Genomically Confirmed Cross Transmission Clusters

2020 ◽  
Vol 41 (S1) ◽  
pp. s439-s440
Author(s):  
Kyle Hansen ◽  
Richard T. Ellison ◽  
Doyle V. Ward ◽  
Devon J. Holler ◽  
Judy L. Ashworth ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Clinical Data ◽  
Medical Center ◽  
Academic Medical Center ◽  
Automated Analysis ◽  
Sample Collection ◽  
Single Nucleotide Variants ◽  
Cross Transmission ◽  
Unique Strain ◽  
Genomic Relatedness

Background: Infection prevention surveillance for cross transmission is often performed by manual review of microbiologic culture results to identify geotemporally related clusters. However, the sensitivity and specificity of this approach remains uncertain. Whole-genome sequencing (WGS) analysis can help provide a gold-standard for identifying cross-transmission events. Objective: We employed a published WGS program, the Philips IntelliSpace Epidemiology platform, to compare accuracy of two surveillance methods: (i.) a virtual infection practitioner (VIP) with perfect recall and automated analysis of antibiotic susceptibility testing (AST), sample collection timing, and patient location data and (ii) a novel clinical matching (CM) algorithm that provides cluster suggestions based on a nuanced weighted analysis of AST data, timing of sample collection, and shared location stays between patients. Methods: WGS was performed routinely on inpatient and emergency department isolates of Enterobacter cloacae, Enterococcus faecium, Klebsiella pneumoniae, and Pseudomonas aeruginosa at an academic medical center. Single-nucleotide variants (SNVs) were compared within core genome regions on a per-species basis to determine cross-transmission clusters. Moreover, one unique strain per patient was included within each analysis, and duplicates were excluded from the final results. Results: Between May 2018 and April 2019, clinical data from 121 patients were paired with WGS data from 28 E. cloacae, 21 E. faecium, 61 K. pneumoniae, and 46 P. aeruginosa isolates. Previously published SNV relatedness thresholds were applied to define genomically related isolates. Mapping of genomic relatedness defined clusters as follows: 4 patients in 2 E. faecium clusters and 2 patients in 1 P. aeruginosa cluster. The VIP method identified 12 potential clusters involving 28 patients, all of which were “pseudoclusters.” Importantly, the CM method identified 7 clusters consisting of 27 patients, which included 1 true E. faecium cluster of 2 patients with genomically related isolates. Conclusions: In light of the WGS data, all of the potential clusters identified by the VIP were pseudoclusters, lacking sufficient genomic relatedness. In contrast, the CM method showed increased sensitivity and specificity: it decreased the percentage of pseudoclusters by 14% and it identified a related genomic cluster of E. faecium. These findings suggest that integrating clinical data analytics and WGS is likely to benefit institutions in limiting expenditure of resources on pseudoclusters. Therefore, WGS combined with more sophisticated surveillance approaches, over standard methods as modeled by the VIP, are needed to better identify and address true cross-transmission events.Funding: This study was supported by Philips Healthcare.Disclosures: None

scholarly journals Research data services in veterinary medicine libraries

2017 ◽  
Vol 104 (4) ◽  
Author(s):  
Erin E. Kerby, MSI
Keyword(s):  
Veterinary Medicine ◽  
Data Management ◽  
Online Survey ◽  
Research Data ◽  
Management Support ◽  
Job Responsibilities ◽  
Data Services ◽  
Medical Libraries ◽  
Definition Of ◽  
Interest In Research

Objective: The study investigated veterinary medicine librarians’ experience with and perceptions of research data services. Many academic libraries have begun to offer research data services in response to researchers’ increased need for data management support. To date, such services have typically been generic, rather than discipline-specific, to appeal to a wide variety of researchers.Methods: An online survey was deployed to identify trends regarding research data services in veterinary medicine libraries. Participants were identified from a list of contacts from the MLA Veterinary Medical Libraries Section.Results: Although many respondents indicated that they have a professional interest in research data services, the majority of veterinary medicine librarians only rarely or occasionally provide data management support as part of their regular job responsibilities. There was little consensus as to whether research data services should be core to a library’s mission despite their perceived importance to the advancement of veterinary research. Furthermore, most respondents stated that research data services are just as or somewhat less important than the other services that they provide and feel only slightly or somewhat prepared to offer such services.Conclusions: Lacking a standard definition of ‘‘research data’’ and a common understanding of precisely what research data services encompass, it is difficult for veterinary medicine librarians and libraries to define and understand their roles in research data services. Nonetheless, they appear to have an interest in learning more about and providing research data services.

scholarly journals Adapting data management education to support clinical research projects in an academic medical center

2019 ◽  
Vol 107 (1) ◽  
Author(s):  
Kevin B. Read
Keyword(s):  
Best Practices ◽  
Clinical Research ◽  
Data Management ◽  
Data Transfer ◽  
Medical Center ◽  
Academic Medical Center ◽  
Research Process ◽  
Academic Medical ◽  
Collaborative Training

Background: Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown.Case Presentation: Leveraging the author’s academic library’s experience supporting CRDM through informationist grants and REDCap training in our medical center, we developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library’s existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop.Conclusions: The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.

scholarly journals Access to Routinely Collected Clinical Data for Research: A Process Implemented at an Academic Medical Center

2019 ◽  
Vol 12 (3) ◽  
pp. 231-235
Author(s):  
Susan C. Guerrero ◽  
Sujatha Sridhar ◽  
Cynthia Edmonds ◽  
Christina F. Solis ◽  
Jiajie Zhang ◽  
...  
Keyword(s):  
Clinical Data ◽  
Medical Center ◽  
Academic Medical Center ◽  
Academic Medical

scholarly journals Data Day to Day: building a community of expertise to address data skills gaps in an academic medical center

2017 ◽  
Vol 105 (2) ◽  
Author(s):  
Alisa Surkis, PhD, MLS ◽  
Fred Willie Zametkin LaPolla, MLS ◽  
Nicole Contaxis, MLIS ◽  
Kevin B. Read, MLIS, MAS
Keyword(s):  
New York ◽  
Medical Center ◽  
Academic Medical Center ◽  
Library Services ◽  
New York University ◽  
Data Services ◽  
Academic Medical ◽  
Address Data ◽  
High Turnout ◽  
Subject Areas

Background: The New York University Health Sciences Library data services team had developed educational material for research data management and data visualization and had been offering classes at the request of departments, research groups, and training programs, but many members of the medical center were unaware of these library data services. There were also indications of data skills gaps in these subject areas and other data-related topics.Case Presentation: The data services team enlisted instructors from across the medical center with data expertise to teach in a series of classes hosted by the library. We hosted eight classes branded as a series called “Data Day to Day.” Seven instructors from four units in the medical center, including the library, taught the classes. A multipronged outreach approach resulted in high turnout. Evaluations indicated that attendees were very satisfied with the instruction, would use the skills learned, and were interested in future classes.Conclusions: Data Day to Day met previously unaddressed data skills gaps. Collaborating with outside instructors allowed the library to serve as a hub for a broad range of data instruction and to raise awareness of library services. We plan to offer the series three times in the coming year with an expanding roster of classes.

Integration of genomic and clinical data augments surveillance of healthcare-acquired infections

2019 ◽  
Vol 40 (6) ◽  
pp. 649-655 ◽  
Author(s):  
Doyle V. Ward ◽  
Andrew G. Hoss ◽  
Raivo Kolde ◽  
Helen C. van Aggelen ◽  
Joshua Loving ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Infection Control ◽  
Clinical Data ◽  
Medical Center ◽  
Academic Medical Center ◽  
Whole Genome Sequence ◽  
False Alarms ◽  
Whole Genome ◽  
Healthcare Settings ◽  
Cross Transmission

AbstractBackground:Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.Objective:To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.Methods:Clinical isolates ofStaphylococcus aureus,Enterococcus faecium,Pseudomonas aeruginosa, andKlebsiella pneumoniaewere obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.Results:Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.Conclusions:Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.

scholarly journals A model for initiating research data management services at academic libraries

2019 ◽  
Vol 107 (3) ◽  
Author(s):  
Kevin B. Read ◽  
Jessica Koos ◽  
Rebekah S. Miller ◽  
Cathryn F. Miller ◽  
Gesina A. Phillips ◽  
...  
Keyword(s):  
Data Management ◽  
Research Data ◽  
Data Series ◽  
Pilot Program ◽  
Data Services ◽  
Research Data Management ◽  
Hands On ◽  
Medical Libraries ◽  
Training Resources ◽  
Educational Modules

Background: Librarians developed a pilot program to provide training, resources, strategies, and support for medical libraries seeking to establish research data management (RDM) services. Participants were required to complete eight educational modules to provide the necessary background in RDM. Each participating institution was then required to use two of the following three elements: (1) a template and strategies for data interviews, (2) a teaching tool kit to teach an introductory RDM class, or (3) strategies for hosting a data class series.Case Presentation: Six libraries participated in the pilot, with between two and eight librarians participating from each institution. Librarians from each institution completed the online training modules. Each institution conducted between six and fifteen data interviews, which helped build connections with researchers, and taught between one and five introductory RDM classes. All classes received very positive evaluations from attendees. Two libraries conducted a data series, with one bringing in instructors from outside the library.Conclusion: The pilot program proved successful in helping participating librarians learn about and engage with their research communities, jump-start their teaching of RDM, and develop institutional partnerships around RDM services. The practical, hands-on approach of this pilot proved to be successful in helping libraries with different environments establish RDM services. The success of this pilot provides a proven path forward for libraries that are developing data services at their own institutions.

scholarly journals Potential Roles for Science Librarians in Research Data Management: A Gap Analysis

2021 ◽  
Author(s):  
Wade Bishop ◽  
Hannah Collier ◽  
Ashley Marie Orehek ◽  
Monica Ihli
Keyword(s):  
Data Management ◽  
Gap Analysis ◽  
Research Integrity ◽  
Research Data ◽  
Management Support ◽  
Job Descriptions ◽  
Data Services ◽  
Data Intensive ◽  
Research Data Management ◽  
Role Based

As many sciences move to be more data-intensive, some science librarians are offering more research data services and perform research data management roles. Job analyses provide insight and context to the tasks employees actually do versus what their job descriptions depict or employers assume. Two separate job analyses studies investigated the roles and responsibilities of data services librarians and research integrity officers among the top 10 private and top 10 public higher education institutions. The focus of these interviews was research data management support roles. Comparing these two groups’ responses indicates that the role-based responsibilities for research data services are not always clear within institutions and are predominantly placed on individual researchers or research teams, but science librarians may provide some solutions to address this gap. This paper presents a model for the potential roles of science librarians in research data management.

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